docstr: convert the sentences into lists of tokens, pad them to the same length, add bos tokens if it is needed
  Args:
      questions (List[str]): list of input questions in str format.
      max_len (int): max number of tokens to generate.
      add_BOS (bool): whether to add the BOS token at the beginning
  Returns:
      Tuple[torch.Tensor], the tokenized and padded torch tensor and the token context length tensor.
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code: def tokenize_batch(self, questions, max_len, add_BOS):
      """
      convert the sentences into lists of tokens, pad them to the same length, add bos tokens if it is needed
      Args:
          questions (List[str]): list of input questions in str format.
          max_len (int): max number of tokens to generate.
          add_BOS (bool): whether to add the BOS token at the beginning
      Returns:
          Tuple[torch.Tensor], the tokenized and padded torch tensor and the token context length tensor.
      """
      tokenizer = self.model.tokenizer
      all_lookups = self.service.get_knn(questions, 1 + self.neighbors)
      # hack to add "source: " tag
      prepend_ids = np.array(tokenizer.text_to_ids("source: "))
      all_lookups = np.pad(
          all_lookups, ((0, 0), (0, 0), (len(prepend_ids), 0))
      )
      all_lookups[:, :, : len(prepend_ids)] = prepend_ids
      all_lookups = all_lookups[:, :, : -len(prepend_ids)]
      reuse_neighbors = all_lookups[:, 1:]
      self.store.set("reuse_neighbors", pickle.dumps(reuse_neighbors))
      neighbor_tokens = [
          neighbors[0].tolist() for neighbors in all_lookups
      ]
  
      # combine question and context
      context_tokens = [
          n + tokenizer.text_to_ids("\nquestion: " + q + " \nanswer:")
          for n, q in zip(neighbor_tokens, questions)
      ]
  
      if add_BOS:
          context_tokens = [
              [tokenizer.bos_id] + s for s in context_tokens
          ]
      if self.pad_token_for_retrieval:
          padded = []
          for line in context_tokens:
              pad_len = (
                  self.chunk_size - len(line) % self.chunk_size
              ) % self.chunk_size
              if self.megatron_lm_compatible:
                  padded.append([tokenizer.eos_id] * pad_len + line)
              else:
                  padded.append([tokenizer.pad_id] * pad_len + line)
          context_tokens = padded
      context_tokens, context_lengths = pad_batch(
          context_tokens, tokenizer.eos_id, max_len
      )
      context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
      context_length_tensor = torch.cuda.LongTensor(context_lengths)
      return context_tokens_tensor, context_length_tensor
  
imports: import json
  import pickle
  from typing import List, Tuple
  import numpy
  import torch
  import torch.distributed
  from nemo.collections.nlp.modules.common.lm_utils import pad_batch
  from nemo.collections.nlp.modules.common.megatron.retrieval_services.retrieval_service import ComboRetrievalService
  from nemo.collections.nlp.modules.common.text_generation_strategy import TextGenerationStrategy
label_desc: Use image node_id 1 for calling the RetroQAModelTextGenerationStrategy obj's underlying member method code with example usage: obj.tokenize_batch(questions, max_len, add_BOS) and returns: context_tokens_tensor,  context_length_tensor